Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies on a shared experience of the world. It is this shared experience that makes utterances meaningful. Natural language processing is a diverse field, and progress throughout its development has come from new representational theories, modeling techniques, data collection paradigms, and tasks. We posit that the present success of representation learning approaches trained on large, text-only corpora requires the parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication.

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At the outset of World War II, you'd have a better chance of finding a needle in a haystack with a camel stuck in its eye than you did shooting down an enemy aircraft in your first dozen or so shots. This is because anti-aircraft shells at the time used manual fuses that had to be dialed in for specific lengths of time to delay their explosion. The idea was that you'd estimate where the targeted plane would be in, say five seconds, based on its currently flight path, then time the shell for that length, fire the shell at the plane and hope that the timing and location were close enough that shrapnel from the exploding shell hits the plane. If your calculations were off by even a hair, the shell would miss by thousands of feet. And if shooting down piloted aircraft was this hard, intercepting Germany's terrifyingly fast V1 and V2 rockets required far more luck than skill. But that's exactly what the team at Section T set out to do.

Written by Camylle Lanteigne (@CamLante), who's currently pursuing a Master's in Public Policy at Concordia University and whose work on social robots and empathy has been featured on Vox. This explainer was written in response to colleagues' requests to know more about temporal bias in AI ethics. It begins with a refresher on cognitive biases, then dives into: how humans understand time, time preferences, present-day preference, confidence changes, planning fallacies, and hindsight bias. Bias is a really big topic, but I'll try to succinctly define a subsection of it--implicit cognitive bias--in a way that is useful for AI ethics, particularly. Humans have cognitive biases, which means every one of us, to varying degrees, holds beliefs and impressions that are not backed up by fleshed out reasoning or evidence, or that we never bothered questioning in the first place.¹

After the stock market closed today, Five9 announced the acquisition of intelligent virtual agent (IVA) company Inference Solutions. The purchase price is $172 million, $148 million in cash and $24 million when certain bookings targets are met. Inference brings 550 customers, among them several joint Five9 customers -- including Chick-fil-A and Wyndham Hotels. Inference was founded in 2005, spun out from Telstra Research Labs -- think of it as the Australian version of Bell Labs. Headquartered in San Francisco, the company has additional offices in Austin, TX and Melbourne, Australia.

In real-world applications, data do not reflect the ones commonly used for neural networks training, since they are usually few, unlabeled and can be available as a stream. Hence many existing deep learning solutions suffer from a limited range of applications, in particular in the case of online streaming data that evolve over time. To narrow this gap, in this work we introduce a novel and complex setting involving unsupervised meta-continual learning with unbalanced tasks. These tasks are built through a clustering procedure applied to a fitted embedding space. We exploit a meta-learning scheme that simultaneously alleviates catastrophic forgetting and favors the generalization to new tasks. Moreover, to encourage feature reuse during the meta-optimization, we exploit a single inner loop taking advantage of an aggregated representation achieved through the use of a self-attention mechanism. Experimental results on few-shot learning benchmarks show competitive performance even compared to the supervised case. Additionally, we empirically observe that in an unsupervised scenario, the small tasks and the variability in the clusters pooling play a crucial role in the generalization capability of the network. Further, on complex datasets, the exploitation of more clusters than the true number of classes leads to higher results, even compared to the ones obtained with full supervision, suggesting that a predefined partitioning into classes can miss relevant structural information.

Brisbane-based robotics company Lyro Robotics has successfully deployed its robots and world-leading picking and packing technology in commercial trials held at the avocado facility, Sunnyspot Packhouse, in Ravensbourne, Queensland. Summer harvest looms, and so does a widespread labour shortage for much of Australia's horticulture industry. The Australian Fresh Produce Alliance (AFPA) indicated in September the country's fruit and vegetable industry was facing a workforce shortage of up to 26,000 people throughout the peak summer season. This figure was reached by Ernst & Young. This is where robotics technology, such as Lyro Robotics, could provide solutions assisting farmers with seasonal and short-term farm work.

A stellar flare is a sudden flash of increased brightness on a star. Young stars are prone to these flares which can incinerate everything around them, including the atmospheres of nearby planets starting to form. Finding out how often young stars erupt can help scientists understand where to look for habitable planets. But until now, searching for these flares involved poring over thousands of measurements of star brightness variations, called'light curves', by eye. Now, an international team of scientists based in Australia and the USA have used machine learning to make the search faster and more effective.

The "Thinking, Fast and Slow" paradigm of Kahneman proposes that we use two different styles of thinking -- a fast and intuitive System 1 for certain tasks, along with a slower but more analytical System 2 for others. While the idea of using this two-system style of thinking is gaining popularity in AI and robotics, our work considers how to interleave the two styles of decision-making, i.e., how System 1 and System 2 should be used together. For this, we propose a novel and general framework which includes a new System 0 to oversee Systems 1 and 2. At every point when a decision needs to be made, System 0 evaluates the situation and quickly hands over the decision-making process to either System 1 or System 2. We evaluate such a framework on a modified version of the classic Pac-Man game, with an already-trained RL algorithm for System 1, a Monte-Carlo tree search for System 2, and several different possible strategies for System 0. As expected, arbitrary switches between Systems 1 and 2 do not work, but certain strategies do well. With System 0, an agent is able to perform better than one that uses only System 1 or System 2.